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New Neural Network Methods for Forecasting Regional Employment: An Analysis of German Labour Markets

Author

Listed:
  • Roberto Patuelli

    (Department of Spatial Economics, Vrije Universiteit Amsterdam)

  • Aura Reggiani

    (Department of Economics, University of Bologna, Italy)

  • Peter Nijkamp

    (Department of Spatial Economics, Vrije Universiteit Amsterdam)

  • Uwe Blien

    (Institut für Arbeitsmarkt und Berufsforschung (IAB), Nuremberg)

Abstract

In this paper, a set of neural network (NN) models is developed to compute short-term forecasts of regional employment patterns in Germany. NNs are modern statistical tools based on learning algorithms that are able to process large amounts of data. NNs are enjoying increasing interest in several fields, because of their effectiveness in handling complex data sets when the functional relationship between dependent and independent variables is not explicitly specified. The present paper compares two NN methodologies. First, it uses NNs to forecast regional employment in both the former West and East Germany. Each model implemented computes single estimates of employment growth rates for each German district, with a 2-year forecasting range. Next, additional forecasts are computed, by combining the NN methodology with Shift-Share Analysis (SSA). Since SSA aims to identify variations observed among the labour districts, its results are used as further explanatory variables in the NN models. The data set used in our experiments consists of a panel of 439 German districts. Because of differences in the size and time horizons of the data, the forecasts for West and East Germany are computed separately. The out-of-sample forecasting ability of the models is evaluated by means of several appropriate statistical indicators.

Suggested Citation

  • Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Uwe Blien, 2006. "New Neural Network Methods for Forecasting Regional Employment: An Analysis of German Labour Markets," Tinbergen Institute Discussion Papers 06-020/3, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20060020
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    References listed on IDEAS

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    1. Nijkamp, Peter & Reggiani, Aura & Tsang, Wai Fai, 2004. "Comparative modelling of interregional transport flows: Applications to multimodal European freight transport," European Journal of Operational Research, Elsevier, vol. 155(3), pages 584-602, June.
    2. Suahasil Nazara & Geoffrey J.D. Hewings, 2004. "Spatial Structure and Taxonomy of Decomposition in Shift‐Share Analysis," Growth and Change, Wiley Blackwell, vol. 35(4), pages 476-490, September.
    3. Uwe Blien & Katja Wolf, 2002. "Regional development of employment in eastern Germany: an analysis with an econometric analogue to shift-share techniques," Papers in Regional Science, Springer;Regional Science Association International, vol. 81(3), pages 391-414.
    4. Edgar S. Dunn, 1960. "A Statistical And Analytical Technique For Regional Analysis," Papers in Regional Science, Wiley Blackwell, vol. 6(1), pages 97-112, January.
    5. Lutz Bellmann & Uwe Blien, 2001. "Wage Curve Analyses of Establishment Data from Western Germany," ILR Review, Cornell University, ILR School, vol. 54(4), pages 851-863, July.
    6. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
    7. Daniel A. Griffith, 2003. "Spatial Autocorrelation and Spatial Filtering," Advances in Spatial Science, Springer, number 978-3-540-24806-4, Fall.
    8. Esteban-Marquillas, J. M., 1972. "I. A reinterpretation of shift-share analysis," Regional and Urban Economics, Elsevier, vol. 2(3), pages 249-255, October.
    9. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December.
    10. John Cooper, 1999. "Artificial neural networks versus multivariate statistics: An application from economics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 909-921.
    11. Longhi, Simonetta & Nijkamp, Peter & Reggiani, Aura & Blien, Uwe, 2002. "Forecasting regional labour markets in Germany: an evaluation of the performance of neural network analysis," ERSA conference papers ersa02p117, European Regional Science Association.
    12. M M Fischer, 1998. "Computational Neural Networks: A New Paradigm for Spatial Analysis," Environment and Planning A, , vol. 30(10), pages 1873-1891, October.
    13. Matías Mayor Fernández & Ana Jesús López Menéndez, 2005. "The spatial shift-share analysis - new developments and some findings for the Spanish case," ERSA conference papers ersa05p659, European Regional Science Association.
    14. Baker, Bruce D. & Richards, Craig E., 1999. "A comparison of conventional linear regression methods and neural networks for forecasting educational spending," Economics of Education Review, Elsevier, vol. 18(4), pages 405-415, October.
    15. Kemble Stokes, H. Jr., 1974. "Shift share once again," Regional and Urban Economics, Elsevier, vol. 4(1), pages 57-60, June.
    16. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Gian Zaccomer & Pamela Mason, 2011. "A new spatial shift-share decomposition for the regional growth analysis: a local study of the employment based on Italian Business Statistical Register," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(3), pages 329-356, August.
    2. Schanne, N. & Wapler, R. & Weyh, A., 2010. "Regional unemployment forecasts with spatial interdependencies," International Journal of Forecasting, Elsevier, vol. 26(4), pages 908-926, October.
    3. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Norbert Schanne, 2011. "Neural networks for regional employment forecasts: are the parameters relevant?," Journal of Geographical Systems, Springer, vol. 13(1), pages 67-85, March.
    4. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2011. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," International Regional Science Review, , vol. 34(2), pages 253-280, April.
    5. Matthias Firgo & Oliver Fritz, 2017. "Does having the right visitor mix do the job? Applying an econometric shift-share model to regional tourism developments," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(3), pages 469-490, May.
    6. Jean‐François Ruault & Yves Schaeffer, 2020. "Scalable shift‐share analysis: Novel framework and application to France," Papers in Regional Science, Wiley Blackwell, vol. 99(6), pages 1667-1690, December.
    7. Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).
    8. Buda, Rodolphe, 2008. "Estimation de l'emploi régional et sectoriel salarié français : application à l'année 2006 [Estimation of the french salaried regional and sectoral employment: application to the year 2006]," MPRA Paper 34881, University Library of Munich, Germany.
    9. Constantin Ilie & Margareta Ilie, 2022. "Brief Analysis of the Evolution of Female Employees in Recent Years. Research Using Mathematical Modelling," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 591-597, September.
    10. Katharina Hampel & Marcus Kunz & Norbert Schanne & Ruediger Wapler & Antje Weyh, 2006. "Regional Unemployment Forecasting Using Structural Component Models With Spatial Autocorrelation," ERSA conference papers ersa06p196, European Regional Science Association.

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    More about this item

    Keywords

    networks; forecasts; regional employment; shift-share analysis; shift-share regression;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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